Machine Learning Engineer Forums

Learning Methods
Subforums: Naive Bayes, Linear Regression, Support Vector Machines, Support Vector Regression, Polynomial Regression, Logistic Regression, k-Nearest Neighbors, Decision Trees, Random Forests, Ensemble Methods, Neural Networks, Feedforward Neural Networks, Convolutional Neural Networks (CNNs), CNN Architecture, LeNet-5, AlexNet, GoogLeNet, ResNet, Recurrent Neural Networks (RNNs), Autoencoders, Single-Stacked Autoencoders, Stacked Autoencoders, Sparse Autoencoders, Variational Autoencoders, Contractive Autoencoder (CAE), Stacked Convolutional Autoencoders, Generative Stochastic Network (GSN), Winner-Take-All (WTA) Autoencoders, Adversarial Autoencoders, Long-Short Term Memory (LSTM) Cells, Restricted Boltzmann Machines (RBMs), (Unsupervised) Generative Adversarial Networks (GANs), Apriori, Eclat, k-Means Clustering, Hierarchical Cluster Analysis, Expectation Maximization, Visualization and Dimensionality Reduction, Principal Component Analysis (PCA), Kernel PCA, Locally-Linear Embedding (LLE), t-SNE, Generative Adversarial Networks (GANs), Reinforcement Learning, Policy Search, AI Gyms, Policy Gradients, Markov Decisions, Temporal Difference, Q-Learning, Semi-Supervised Learning, Self-Supervised Learning

Process
Subforums: Data Collection, Data Preparation, Exploratory Data Analysis (EDA), Data Pre-Processing, Feature Encoding, Feature Normalization, Feature Engineering, Feature Selection, Feature Imputation, Dealing with Imbalances, Data Splitting, Training Model, Choosing an Algorithm, Supervised Algorithms, Unsupervised Algorithms, Types of Learning, Batch Learning, Online Learning, Transfer Learning, Active Learning, Ensembling, Underfitting, Overfitting, Regularization, Hyperparameter Tuning, Analysis/Evaluation, Evaluation Metrics, Feature Importance, Training/Inference Time/Cost, What-if Tool, Least Confident Examples, Bias/Variance Trade-off, Serving Model, Available Tools, Making Predictions, Retraining Model

Applications
Subforums: Natural Language Processing (NLP), Word2Vec, GloVe, ELMo, BERT, Image Recognition, Sentiment Analysis, Speech Recognition, Deep Learning

Python
Subforums: Tutorials, Best Practices, 1. Computers and Programs, 2. Variables and Assignments, 3. String Basics, 4. Loops, 5. If-else Branches, 6. Lists, 7. User Defined Functions, 8. Classes, 9. Modules, Variables, Conditions, Chained Conditionals, Operators, Control Flow (If/else), Loops and Iterables, Basic Data Structure, Functions, Mutability, Common Methods, File IO, Object Oriented Programming (OOP), Data Structures, Comprehensions, Lambda Functions, Collections Module, Map & Filter, ARGs & KWARGS, Inheritance, Advanced Class Behavior, Dunder Methods, PIP, Environments, Custom Modules, Async IO, Decorators, Generators, Context Managers, Metaclasses, Concurrency, Parallelism, Testing, Data Types, Assignment Operators, Commenting, Lists, Sets, Dictionaries, Logical Operators, Loops, Functions, Classes & Objects, Methods, Working with Dates, Working with Files, PIP & Modules, Iterators & Generators, Typecasting, Tuples, Exception Handling, Encapsulation, Data Abstraction, Polymorphism, Environment Variables, Inheritance, Numpy, Pandas, Matplotlib, Scikit-Learn, TensorFlow/Keras, PyTorch, Kivy, QT, TKinter, CherryPy, TurboGears, Web2Py, Caffe, DASK, Django, Flask, SciPy, Seaborn, PySpark

Web Development
Subforums: HTML, CSS

Mathematics
Subforums: Calculus, Differential Calculus, Integral Calculus, Linear Algebra, Matrices, Vectors, Probability & Statistics, Probability Distributions, Linear Models, Bayesian Statistics, Multivariate Statistics, Time Series, Sampling, Hypothesis Testing, Algorithms, Data Structures, Search Methods, Sorting, Lists, Stacks, Queues, Lists, Stacks, Queues, Hash Tables, Heaps, Treaps, Hash Tables, Heaps, Treaps, Trees, Binary Trees, AVL Trees, Red-Black Trees, Graphs, Sets and Additional Sorts, B-trees